The few design the future. The rest of us inherit it.

Start with a question of your own

Meanwhile, the rest of us are asking:

• What happens if I sign this lease?
• Where should I park my food truck this week?
• Where should our three pizza shops go so deliveries work on a busy Friday?
• If they close the bridge for repairs, how long will my drive to work take?
• If we open on Sundays, who would come?
• How many hours of dangerous heat would we prevent by shading that bus stop?
• Which storm drains on our block should we clear before the next downpour?

Whathappensify is urban simulation for the rest of us. An AI you choose turns your question into a simulation. Whathappensify runs it, and the AI explains the result, what it means for your question and the evidence behind it. You see the work in your browser and direct what happens next.

Your AI assistant does the specialist work: finding suitable data and methods, preparing scenarios, running comparisons and interpreting the results. You can inspect its choices, change an assumption and pursue another possibility. Whose questions get explored should no longer depend on who can operate simulation software.

Rosa chooses a spot for her pupusa truck

Imagine Rosa choosing where to park her pupusa truck. The station is busy in the morning, but those commuters are hurrying to catch a train. Near another spot, people come out of their workplaces at lunch. A third spot is busiest when neighbors shop on the weekend. Which of these crowds is most likely to stop for pupusas?

What happens on a day when more people work from home? A spot that looks promising on a busy office day may do better or worse when the offices are half empty. Rosa can try another location or another day before she commits her time and ingredients.

Follow Rosa’s question · Read her imagined story

Follow the reasoning

The Epistemic Protocol Machine, or EPM, lets an assistant take your question further and keeps a record of the evidence and assumptions behind each step. Rosa can ask why one spot looks promising, where the estimate of nearby customers came from, and how the choice changes on a quieter office day. The EPM keeps every answer within the limits of its evidence.

You can ask what produced a difference, which assumption mattered most, how strongly the evidence supports the result and what would change the conclusion. The record gives you a basis for examining the answer and taking the question further.

Explore the Epistemic Protocol Machine

From one moment to lasting change

A question can reach from the next few minutes to the next twenty years. A queue outside a clinic changes through the morning. Staff training, maintenance and investment change what that clinic can provide over years. Trees grow, infrastructure wears, people change their journeys, and today's choice changes tomorrow's conditions.

Whathappensify connects these processes in one experiment. Your assistant uses suitable physical and network models, draws on shared models or writes an inspectable model of the mechanism you want to explore. You approve the alternatives and scope, watch the work in your browser and follow how the consequences pass from one model to another.

The comparison shows which assumptions matter, how outcomes vary and who gains or loses. You can ask whether a conclusion survives a different assumption, which feedback produced a change, or what further observation could help you decide. Useful models, data and experience can be kept private or shared for others to build on.

Whose idea of better?

People can agree about a simulation result and care about different consequences. Rosa needs enough customers to sustain her business. Someone using a wheelchair needs room to reach the truck and order. A neighbor cares about customers blocking the sidewalk. Perspectives make those concerns explicit: what matters to someone, why it matters, and how the simulation represents it.

You can compare Perspectives, question a measure and propose a different definition of success. A concern stays on the record even when the model cannot express it as a number. You can examine both the reasoning and the purposes behind a comparison. People can work from shared evidence and still keep different priorities.

See one result through different Perspectives

One shade structure, three possible places

Leah helps look after a small town's public spaces. Her team has a modest budget and no simulation specialists. They can put up one shade structure before next summer. Where could it do the most good?

She asks her AI assistant to compare the library entrance, the public square and the playground. The busiest place changes through the day. So does the shade. And a place to wait comfortably can matter very differently to an older neighbor and to a family coming out after school.

Small towns make the same kinds of decisions as large cities, about bus routes, parking rules, opening hours and new services, usually without paying for a consultant's study. Accessible simulation could help a small team explore a choice, bring local knowledge into it and share the approach with another town. More people can shape the places where they live and work.

More people shaping what happens next

A prospective shopkeeper can compare locations before signing a lease. Neighbors can explore how to protect their building during a heat wave. As the climate changes, the people who live with the heat and the floodwater are often the first to see what could help. A volunteer can redesign a market so older shoppers can reach it. The questions, the local knowledge and the initiative come from people.

Explore more imagined uses · Grace prepares for a heat wave · Amara’s block keeps the rain out · Jamal’s tenants examine a rail proposal

You decide whether to keep your work private or share it. A market organizer could publish alternatives for traders and neighbors to explore through their own Perspectives. Someone else could make a fork, their own editable copy of the work, and try a different layout. An organizer across town could borrow the approach, using information about their own neighborhood.

Useful work can accumulate: simulations, prepared data, methods, Perspectives and experience that help the next person begin further along. People can return to what they explored, compare it with what actually happened and learn together.

Simulate back.

Democracy is how we decide our future. The power to explore possible futures should belong to all of us.

When a plan affects your home or livelihood, you should be able to examine its assumptions and develop an alternative of your own. If you disagree with a simulation someone has shared, change the assumption you dispute and explore what follows. Bring it to that hearing. Answer in kind. Residents, businesses and public servants can also use shared work to find a better arrangement while there is still time to shape it.

Nobody built this for us. We were never the customer. United Cities Foundation provides it as free, open-source public infrastructure, yours to examine and extend. People can initiate ideas, learn from one another and help decide what happens next.

How answering in kind could change a city

See the work

Whathappensify connects simulation, AI assistants, reasoning that people can examine, and Perspectives that people can make their own. The screens show how you ask a question, examine a model, approve an experiment and explore its results. Open a screen to examine it more closely. One scenario can combine a physical model, a stock-and-flow model you write and network analysis over the same place.

The worked Perspective example shows how different points of view change what the same constructed result means. The technical reference explains the reasoning rules, calculations and detailed examples behind the design.

AI, simulation, the EPM, Perspectives and shared infrastructure serve one purpose: to let more people help decide what happens next.